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Last updated:

September 23, 2023

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This course includes:

Unlimited Duration

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Description

Linear Regression Analysis and Forecasting. Instructor: Prof. Shalabh, Department of Mathematics and Statistics, IIT Kanpur.

Forecasting is an important aspect of any experimental study. The forecasting can be done by finding the model between the input and output variables. The tools of linear regression analysis help in finding out a statistical model between input variables and output variable which in turn provides forecasting. For example, the yield of a crop depends upon the area of crop, quantity of seeds, rainfall etc. The statistical relation between yield and area of crop, quantity of seeds, rainfall etc. can be determined by the regression analysis and forecasting can be done to know the yield in future. The accuracy of forecasting depends upon the goodness of obtained model. What are its steps and checks required to obtain a good model and in turn, how to do forecasting is being aimed to be taught in this course. (from nptel.ac.in)

Course Curriculum

  • Lecture 01 – Basic Fundamental Concepts of Modeling Unlimited
  • Lecture 02 – Regression Model – A Statistical Tool Unlimited
  • Lecture 03 – Simple Linear Regression Analysis Unlimited
  • Lecture 04 – Estimation of Parameters in Simple Linear Regression Model Unlimited
  • Lecture 05 – Estimation of Parameters in Simple Linear Regression Model (cont.) Unlimited
  • Lecture 06 – Estimation of Parameters in Simple Linear Regression Model (cont.) Unlimited
  • Lecture 07 – Maximum Likelihood Estimation of Parameters in Simple Linear Regression Model Unlimited
  • Lecture 08 – Testing of Hypothesis and Confidence Interval Estimation in Simple Linear … Unlimited
  • Lecture 09 – Testing of Hypothesis and Confidence Interval Estimation in Simple Linear … Unlimited
  • Lecture 10 – Software Implementation in Simple Linear Regression Model using MINITAB Unlimited
  • Lecture 11 – Multiple Linear Regression Model Unlimited
  • Lecture 12 – Estimation of Model Parameters in Multiple Linear Regression Model Unlimited
  • Lecture 13 – Estimation of Model Parameters in Multiple Linear Regression Model (cont.) Unlimited
  • Lecture 14 – Standardized Regression Coefficients and Testing of Hypothesis Unlimited
  • Lecture 15 – Testing of Hypothesis (cont.), Goodness of Fit of the Model Unlimited
  • Lecture 16 – Diagnostics in Multiple Linear Regression Model Unlimited
  • Lecture 17 – Diagnostics in Multiple Linear Regression Model (cont.) Unlimited
  • Lecture 18 – Diagnostics in Multiple Linear Regression Model (cont.) Unlimited
  • Lecture 19 – Software Implementation of Multiple Linear Regression Model using MINITAB Unlimited
  • Lecture 20 – Software Implementation of Multiple Linear Regression Model using MINITAB (cont.) Unlimited
  • Lecture 21 – Forecasting in Multiple Linear Regression Model Unlimited
  • Lecture 22 – Within Sample Forecasting Unlimited
  • Lecture 23 – Outside Sample Forecasting Unlimited
  • Lecture 24 – Software Implementation of Forecasting using MINITAB Unlimited

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